Attachment_1_CDAO IDIQ PWS_7.17.26.pdf

CONTROLLED UNCLASSIFIED INFORMATION (CUI)

SOURCE SELECTION INFORMATION – SEE FAR 2.101 AND 3.104

DEPARTMENT OF WAR EDUCATION ACTIVITY (DoWEA)

PERFORMANCE WORK STATEMENT (PWS)

FOR

Data, Analytics, and AI Modernization Services

Data and AI Division

1.0 Introduction

The Department of War Education Activity (DoWEA), one of two federally operated school systems, is responsible for planning, directing, coordinating, and managing pre-kindergarten through 12th-grade educational programs for the Department of War (DoW). Globally positioned, DoWEA operates 160+ accredited schools in 9 districts located in 11 foreign countries, 7 states, Guam, and Puerto Rico, serving over 67,000 students and employing approximately 15,000 staff.

The Data and AI Division leads DoWEA’s data, analytics, and artificial intelligence (AI) functions. The Data and AI Division is responsible for data governance, data architecture, analytics, AI integration, data and AI literacy, and Student Information System (SIS) oversight.

1.1 Background

DoWEA is modernizing its enterprise data environment by transitioning to a cloud-based data

Lakehouse. The Government has designated Microsoft Fabric and associated Microsoft services

(including Power BI, Microsoft Purview, and Microsoft Entra ID) as the authorized enterprise analytics and data platform. DoWEA’s current Student Information System is Follett Aspen, and

SIS modernization is a component of the enterprise data modernization roadmap. This designation reflects an existing Government infrastructure decision; this PWS does not require contractors to propose or evaluate alternative platforms. The Ed-Fi Operational Data Store serves as the standardized integration layer for education data, with separate governed pipelines for non-education data domains. Both paths feed into the unified Fabric Lakehouse, which serves as the single analytical and AI-ready platform for the enterprise.

Data and AI Division requires sustained contractor support across multiple functional areas to execute this modernization, build organizational data and AI capability, and maintain operational continuity over a multi-year period.

1.2 Scope

The contractor shall provide all personnel, management, supervision, tools, equipment, and services necessary for DoWEA to procure data, analytics, AI, and related modernization services for the DoWEA Data and AI Division.

The scope encompasses the following functional areas:

• Data Architecture and Engineering

• Analytics and Business Intelligence

• Data Governance

• AI Integration and Solutions

• Data and AI Literacy

• Student Information System (SIS) Modernization Support

• Program Management, Coordination, and Transition Support

The contractor shall provide qualified personnel across all functional areas and shall be capable of scaling support up or down based on task order requirements.

1.3 Task Orders and IDIQ Structure

This is a single-award Indefinite Delivery/Indefinite Quantity (IDIQ) contract. The

Government will issue task orders under this IDIQ to define specific locations, quantities, schedules, and additional or emergent Data and AI Division support requirements.

Each task order will specify applicable PWS objectives, supporting tasks, performance standards, and deliverables. Task orders may also define additional metrics or Acceptable

Quality Levels (AQLs) unique to the work being ordered. In the event of a conflict between

IDIQ PWS and a task order PWS, the task order requirements take precedence for that specific requirement.

The contractor is expected to respond to task orders in a timely manner, integrating personnel and resources as needed, while maintaining compliance with all PWS objectives, performance metrics, and Government technical and security standards.

All work performed under this contract shall comply with applicable DoW, DISA, and

DoWEA policies, cybersecurity requirements, technical standards, and operational procedures.

2.0 Requirement Objectives

The following objectives define the functional areas within which the Government may issue task orders. Each objective describes the category of work, its purpose, and the types of tasks the contractor may be asked to perform. Specific tasks, deliverables, and performance periods will be defined in individual task orders.

The following listed tasks under each objective help the contractor understand the scope of potential requirements but are not exhaustive. The contractor must be prepared to develop complete solutions using a performance-based approach for each task order issued.

2.1 Data Architecture and Engineering (Objective #1)

The objective is to design, build, and maintain a modern, governed, cloud-based data architecture that enables enterprise-wide analytics, AI readiness, and data-driven decision-making across all

DoWEA data domains.

The following tasks help the Contractor understand the requirements to meet the objective but are not exhaustive. The Contractor shall develop a complete solution using a performance-based approach.

Supporting tasks may include:

2.1.1 Design and implement data Lakehouse architecture using Microsoft Fabric, including governed data zones (Raw/Landing, Operational/ODS, Curated/Analytics)

2.1.2 Develop data ingestion pipelines using Azure Data Factory for education and non-education data domains

2.1.3 Implement Ed-Fi ODS integration using the Ed-Fi REST API for education data

2.1.4 Design and implement non-education data ingestion pipelines (HR/DCPDS, finance, logistics, IT service management)

2.1.5 Implement medallion architecture (Bronze/Silver/Gold) with schema validation, data cleansing, normalization, and transformation logic

2.1.6 Configure environment separation (Development, Test, Production) with CI/CD deployment pipelines and infrastructure-as-code

2.1.7 Implement data quality monitoring, reconciliation testing, and automated alerting

2.1.8 Design and implement disaster recovery and business continuity procedures, including rollback testing and tabletop exercises

2.1.9 Support hybrid connectivity between on-premises and cloud environments as required

2.1.10 Migrate legacy data warehouse components to the Fabric environment

2.1.11 Produce and maintain data dictionaries, data lineage documentation, and metadata catalogs

2.1.12 Register data products in Microsoft Purview with sensitivity labels, domain classifications, and ownership metadata

Performance Standards and AQLs (Objective 1)

• Performance Standard 1.1:

o Standard/Measure: Data pipelines operate with ≥99.5% reconciliation accuracy. Curated layer data products meet <2% post-validation error rate.

Data quality checks at each zone transition shall align with the VAULTIS framework (Visible, Accessible, Understandable, Linked, Trustworthy, Interoperable, Secure).

o AQL: No more than two (2) validated data quality incidents per month per data domain.

• Performance Standard 1.2:

o Standard/Measure: Data dictionaries, data lineage documentation, and metadata catalogs are maintained current within 5 business days of any schema change, pipeline modification, or new data product deployment. 100% of production data products are registered in Microsoft Purview with sensitivity labels, domain classification, and designated data owner.

o AQL: No more than one (1) documentation gap (missing or outdated dictionary entry, unregistered data product, or incomplete lineage record) per data domain identified during monthly review.

• Performance Standard 1.3:

o Standard/Measure: All code and configuration changes are deployed through the established CI/CD pipeline. No direct production deployments outside the pipeline except documented emergency changes with COR approval.

Development, Test, and Production environments maintain configuration parity as defined in the architecture baseline.

o AQL: Zero unauthorized production deployments per month. No more than one (1) environment configuration drift finding per quarter.

• Performance Standard 1.4:

o Standard/Measure: Disaster recovery procedures are tested at minimum once per quarter through either rollback testing or tabletop exercise. Recovery Point

Objective (RPO) and Recovery Time Objective (RTO) meet thresholds defined in the task order. Rollback capability is validated after each major deployment.

o AQL: 100% of scheduled DR tests are completed on schedule. Zero failed rollback attempts during validated testing.

• Performance Standard 1.5:

o Standard/Measure: Migrated data domains achieve ≥99.5% record-level reconciliation between legacy source and Fabric target. Migration validation reports are produced for each domain and accepted by the COR prior to legacy system decommission.

o AQL: 100% of migrated data domains achieve ≥99.5% record-level reconciliation between legacy source and Fabric target, validated through independent reconciliation testing prior to production use.

• Performance Standard 1.6:

o Standard/Measure: The contractor shall produce a formal Architecture Design

Document, including target-state data zone structure, data flow diagrams, integration patterns, security architecture, and environment topology, prior to implementation. Implementation shall not proceed until the Architecture

Design Document is reviewed and formally accepted by the COR. Any material deviations from the accepted design during implementation require written COR approval.

o AQL: Zero implementation activities initiated on any data domain prior to

COR acceptance of the applicable architecture design. Zero material deviations from accepted design without documented COR approval.

• Performance Standard 1.7:

o Standard/Measure: Production data pipelines and scheduled refresh processes maintain ≥99% availability during core business hours (Monday–Friday

0730–1600 ET). Automated alerting notifies the contractor and COR within

15 minutes of any pipeline failure or data refresh failure in production.

o AQL: No more than two (2) unplanned production pipeline outages exceeding

4 hours per month. 100% of production failures generate automated alerts within the 15-minute threshold.

• Performance Standard 1.8:

o Standard/Measure: The contractor shall produce monthly data quality reports for each data domain under active population or maintenance. Reports shall document quality metrics against established thresholds, trend analysis, identified issues, and corrective actions taken.

o AQL: 100% of monthly data quality reports delivered by the 5th business day of the following month for all active data domains.

• Performance Standard 1.9:

o Standard/Measure: No data domain or data product shall be promoted to production without completing User Acceptance Testing (UAT) with the designated Government data steward. UAT results shall be documented and the

Government data steward shall provide written acceptance prior to production promotion.

o AQL: Zero data domains or data products promoted to production without documented UAT completion and written Government data steward acceptance.

2.2 Analytics and Business Intelligence (Objective 2)

The objective is to modernize DoWEA’s analytics and reporting environment to deliver timely, reliable, and accessible data products that support decision-making at all organizational levels.

The following tasks help the Contractor understand the requirements to meet the objective but are not exhaustive. The Contractor shall develop a complete solution using a performance-based approach.

Supporting tasks may include:

2.2.1 Assess the current dashboard and reporting portfolio, including usage, value, redundancy, and technical debt

2.2.2 Develop and execute a dashboard consolidation and migration plan

2.2.3 Develop enterprise semantic models and publish certified Power BI datasets

2.2.4 Build executive, operational, and compliance dashboards with <5-second load time under standard workload conditions

2.2.5 Implement row-level security (RLS) and object-level security (OLS) aligned with

DoWEA’s data governance framework

2.2.6 Assess and implement self-service analytics capabilities, including Copilot-assisted analytics and/or other vendor self-service analytics

2.2.7 Develop user documentation and quick-reference guides for analytics products

2.2.8 Assess and recommend Power BI licensing transition strategies (P-SKU to F-

SKU)

Performance Standards and AQLs (Objective 2)

Performance Standard 2.1:

o Standard/Measure: Dashboards load in <5 seconds under standard workload.

Analytics products reflect governed, curated data sources.

o AQL: No more than three (3) validated reporting accuracy or availability incidents per month.

Performance Standard 2.2:

o Standard/Measure: The contractor shall produce a comprehensive dashboard portfolio assessment that inventories 100% of existing dashboards and reports, including ownership, usage frequency, data sources, redundancy mapping, and technical debt classification. The assessment shall result in a consolidation and migration plan with prioritized disposition recommendations (retain, consolidate, retire, rebuild) for each item, accepted by the COR prior to execution.

o AQL: 100% of existing dashboards and reports are inventoried in the portfolio assessment. Consolidation plan achieves a target end state of no more than 20 strategically aligned, enterprise-level production dashboards maintained at the HQ level, with an absolute maximum of 25. Dashboards designated for retirement are decommissioned within 30 days of replacement capability reaching production. No new dashboard shall be created outside the approved portfolio without COR approval.

Performance Standard 2.3:

o Standard/Measure: Enterprise semantic models follow documented naming conventions, use approved curated-layer data sources exclusively, and are published as certified Power

BI datasets with defined refresh schedules. No production dashboard shall consume raw or uncertified data sources.

o AQL: 100% of production dashboards consume only certified datasets. Zero production semantic models referencing Bronze or Silver layer data directly.

Performance Standard 2.4:

Standard/Measure: Row-level security (RLS) and object-level security (OLS) are implemented on 100% of production dashboards and datasets that contain role-restricted or organizationally scoped data, aligned with the DoWEA data governance framework and RBAC model. Security configurations are validated through testing with representative user roles prior to production deployment.

AQL: Zero unauthorized data exposure incidents attributable to missing or misconfigured

RLS/OLS. 100% of security configurations validated through documented testing prior to production release.

Performance Standard 2.5:

o Standard/Measure: User documentation and quick-reference guides are produced for

100% of production analytics products and published to the designated Government

SharePoint site. Documentation is updated within 5 business days of any material change to the associated dashboard or data product.

o AQL: No more than one (1) documentation gap (missing or outdated guide for a production analytics product) identified during monthly review.

2.3 Data Governance (Objective 3)

The objective is to develop, operationalize, and sustain an enterprise data governance framework that ensures data and AI are managed as strategic assets in accordance with DoW mandates, including the implementation of data security controls, data classification, and access governance within the enterprise data platform, using a capacity-building model that progressively transfers execution to Government staff.

The following tasks help the Contractor understand the requirements to meet the objective but are not exhaustive. The Contractor shall develop a complete solution using a

2.3.1 Assess current data governance maturity and develop recommendations

2.3.2 Co-develop governance artifacts with Government staff, including Data

Governance Board charter, data quality standards, data classification policy, access request procedures, metadata standards, and governance review workflows

2.3.3 Provide subject-matter expertise on federal, education, and DoW data governance best practices

2.3.4 Support operationalization of governance processes (data quality reviews, access request processing, governance board facilitation support, etc.)

2.3.5 Develop data retention and lifecycle policies aligned with federal retention requirements

2.3.6 Support Master Data Management (MDM) strategy development

2.3.7 Provide governance coordination support during data population and migration activities

2.3.8 Produce governance maturity progress reports

2.3.9 Support the configuration and maintenance of Microsoft Purview governance controls for the data environment, including sensitivity labels, data domain definitions, access policies, and DLP rules

2.3.10 Support data classification and marking requirements for data products within the

Fabric environment, including CUI marking, sensitivity classifications, privacy markings, and records management tags, in coordination with IT cybersecurity and compliance staff

2.3.11 Configure and validate role-based access controls (RBAC) for data access within the Fabric environment integrated with Microsoft Entra ID, aligned with the

DoWEA data governance framework and RBAC model

2.3.12 Capacity-Building Requirement: The contractor shall ensure that for every governance artifact produced, at least one Government staff member is an active co-developer. The contractor shall not produce governance artifacts in isolation.

The Government retains all decision-making authority.

Performance Standards and AQLs (Objective 3)

Performance Standard 3.1:

• Standard/Measure: Governance artifacts are co-developed with Government staff and are independently maintainable by Government staff upon delivery.

• AQL: 100% of governance artifacts have a designated Government co-developer and include knowledge transfer documentation.

Performance Standard 3.2:

• Standard/Measure: Within 15 business days of task order award, the contractor shall conduct a Governance Implementation Planning Session with the COR and designated

Government staff to review the Government-provided governance checklist, identify gaps or sequencing dependencies, and produce a finalized Governance Implementation

Tracker. The Government-provided checklist is the baseline. The contractor may recommend additions but shall not remove or defer items without COR approval.

Following acceptance, the tracker shall be updated weekly and shall document: artifact or process name, assigned Government co-developer, current status (not started, in development, in review, accepted, operational), target completion date, and any blockers or dependencies. The tracker shall be reviewed with the COR at each monthly status meeting.

• AQL: Governance Implementation Planning Session completed and Governance

Implementation Tracker accepted by COR within 15 business days of task order award.

Tracker updated weekly with 100% accuracy against actual work status. Zero artifacts or processes stalled in the same status for more than 30 days without documented blocker and corrective action plan.

Performance Standard 3.3:

• Standard/Measure: Governance processes defined in the DoWEA Data Governance

Framework are operationalized within the timelines established in the task order. Once operational, recurring governance activities (data quality reviews, access request processing, governance board meetings) are executed on their defined schedule. Access requests are processed within 5 business days of submission. Data quality reviews are completed on schedule for each data domain.

• AQL: 95% of governance board meetings conducted on schedule. 95% of data access requests processed within 5 business days. 100% of scheduled data quality reviews completed within the defined review cycle.

Performance Standard 3.4:

• Standard/Measure: The contractor shall deliver, at minimum, the following governance artifacts as co-developed deliverables: Data Governance Board charter, data classification policy, data quality standards, data access request procedures, metadata standards, governance review workflows, and data retention and lifecycle policies. Each artifact shall align with DoD Data Stewardship Guidebook role hierarchy and DoWEA’s federated governance model.

• AQL: 100% of required governance artifacts delivered and COR-accepted within the timelines established in the applicable task order. Zero governance artifacts delivered without alignment to the DoD Data Stewardship Guidebook role hierarchy unless otherwise directed by the COR.

Performance Standard 3.5:

• Standard/Measure: During data population and migration activities, the contractor shall ensure that governance controls are applied to all data entering the Fabric environment, including data classification, sensitivity labeling, ownership assignment, and access control configuration. No data domain shall be promoted to production in the Fabric environment without completed governance review and COR confirmation of governance compliance.

• AQL: Zero data domains promoted to Fabric production without completed governance review. 100% of production datasets have assigned data owner, classification label, and access controls configured prior to production use.

Performance Standard 3.6:

• Standard/Measure: Microsoft Purview governance controls (sensitivity labels, data domain definitions, access policies, DLP rules) and RBAC configurations for data access are configured in accordance with the DoWEA data governance framework and maintained current as the data environment evolves. Data classification and marking is completed for 100% of production data products prior to production promotion. Security configurations are reviewed and validated after each new data domain onboarding or quarterly, whichever is more frequent. Configuration changes are coordinated with CIO security and compliance staff.

• AQL: 100% of production data products have completed data classification and sensitivity labeling prior to production use. Zero new data domain deployments proceed to production without validated RBAC and Purview configurations. Zero configuration changes implemented without coordination with IT cybersecurity and compliance staff.

2.4 AI Integration and Solutions (Objective 4)

The objective is to support the responsible integration and implementation of artificial intelligence into DoWEA’s educational and operational practices, including the design, development, and deployment of AI-driven solutions and process automations aligned with the

DoWEA’s AI Strategy and Guidelines.

The following tasks help the Contractor understand the requirements to meet the objective but are not exhaustive. The Contractor shall develop a complete solution using a performance-based approach.

Supporting tasks may include:

2.4.1 Conduct AI readiness assessments and feasibility-benefit analyses for proposed

AI use cases

2.4.2 Develop technical supplements to the DoWEA AI Governance Framework, including AI tool evaluation and adoption criteria, model performance monitoring and evaluation protocols, and AI incident detection and response procedures

2.4.3 Support AI tool evaluation and pilot program design

2.4.4 Develop AI-enabled analytics capabilities (predictive modeling, forecasting, natural language query)

2.4.5 Develop a semantic model in the Lakehouse to support AI-enabled analytics

2.4.6 Support procurement readiness for AI tools, including PWS requirements language for AI-related data privacy, security, and transparency

2.4.7 Assess and recommend DoW-developed or acquired AI/LLM platforms (e.g., GenAI.mil,) for DoWEA applicability

2.4.8 Develop and/or implement AI monitoring and evaluation frameworks

2.4.9 Provide advisory services on emerging AI capabilities and their applicability to

K-12 education and DoW operations

2.4.10 Design, develop, and deploy AI-driven process automations for education and business operations workflows using Government-authorized platforms (e.g., Power Automate, Power Apps, Copilot Studio)

2.4.11 Develop and implement AI-enabled solutions through the Data and AI Division

Solution Delivery Process, including intake assessment, solution design, development, testing, and production deployment

2.4.12 Build and maintain AI agents, automated workflows, and intelligent document processing capabilities in coordination with IT and designated functional process owners

2.4.13 Support integration of AI capabilities into existing business processes, including requirements gathering with functional stakeholders, user acceptance testing, and post-deployment adoption support

Performance Standards and AQLs (Objective 4)

Performance Standard 4.1:

• Standard/Measure: AI advisory deliverables align with DoW Responsible AI principles and DAAIS AI Hierarchy of Needs.

• AQL: 100% of AI-related deliverables include risk assessment and responsible use considerations.

Performance Standard 4.2:

• Standard/Measure: AI readiness assessments, feasibility-benefit analyses, and platform evaluations shall follow a standardized evaluation framework accepted by the COR. Each evaluation shall include, at minimum: problem definition, data readiness assessment, technical feasibility analysis, estimated implementation effort, expected benefit to

DoWEA mission outcomes, risk assessment aligned with DoW Responsible AI principles, and a clear recommendation (proceed, proceed with conditions, or do not proceed) with supporting rationale.

• AQL: 100% of AI evaluations follow the accepted evaluation framework with all required elements. Zero AI initiatives proceed to pilot or implementation without a completed and

COR-accepted feasibility-benefit analysis.

Performance Standard 4.3:

• Standard/Measure: AI-enabled capabilities (predictive models, forecasting tools, natural language query, process automations, AI agents, or other AI driven solutions) shall not be deployed to production without completing a structured pilot. Each pilot shall define success criteria, test with representative DoWEA data, include user acceptance testing with designated Government staff, and produce a pilot results report documenting performance against success criteria. The COR shall approve advancement from pilot to production.

• AQL: Zero AI capabilities deployed to production without completed pilot and COR-approved pilot results report. Pilot success criteria are met or a documented remediation plan is accepted by the COR before production deployment.

Performance Standard 4.4:

• Standard/Measure: The contractor shall develop technical supplements to the existing

DoWEA AI Governance Framework, to include: AI tool evaluation and adoption criteria, model performance monitoring and evaluation protocols, and AI incident detection and response procedures. Technical supplements shall be co-developed with Government staff, align with the existing DoWEA governance structure, and be delivered within the timeline established in the applicable task order.

• AQL: 100% of AI Governance Framework technical supplements delivered, co-developed with designated Government staff, and accepted by the COR within task order timelines.

Performance Standard 4.5:

• Standard/Measure: AI-driven solutions and process automations developed under this objective shall follow the Data and AI Division Solution Delivery Process, including documented intake assessment, solution design review with the COR, development in the

Government-designated platform environment, and user acceptance testing with the designated functional process owner. Solutions shall include technical documentation sufficient for Government staff to maintain and modify the solution independently after delivery. No AI-driven solution or automation shall be deployed to production without documented COR approval of the solution design and completed UAT.

• AQL: 100% of AI-driven solutions and automations follow the Data and AI Division

Solution Delivery Process with all required elements. Zero solutions deployed to production without documented COR-approved solution design and completed UAT.

100% of deployed solutions include technical documentation validated as sufficient for independent Government maintenance.

2.5 Data and AI Literacy (Objective 5)

The objective is to assess workforce data and AI literacy, develop role-based literacy frameworks, and create multi-modal on-demand training content that builds organizational capacity for data-driven decision-making.

The following tasks help the Contractor understand the requirements to meet the objective but are not exhaustive. The Contractor shall develop a complete solution using a

2.5.1 Assess current data literacy readiness across DoWEA role groups (school-level staff, above-school leadership, HQ program owners, data/analytics staff, regional education researchers)

2.5.2 Evaluate and recommend enterprise data literacy frameworks (e.g., DQC

Roadmap to Data-Informed Decision-Making)

2.5.3 Develop multi-year data literacy roadmaps with learning objectives, competency progressions, and delivery models by role group

2.5.4 Design and develop on-demand, role-based data literacy training modules using multiple delivery modes (interactive e-learning, written articles, voice-over slide presentations, short-form video) for Data and AI Division -owned literacy domains (e.g. data governance awareness, data quality fundamentals, reading and interpreting dashboards, data stewardship responsibilities, data privacy and responsible handling, introduction to AI literacy for data practitioners)

2.5.5 Develop facilitator guides to enable Government staff to deliver instructor-led versions

2.5.6 Pilot training modules and incorporate feedback before final delivery

2.5.7 Publish all materials to Government SharePoint with unlimited Government rights

2.5.8 Ownership Requirement: The Government shall receive unlimited rights in all training materials, content, facilitator guides, source files, and associated intellectual property in accordance with applicable FAR and DFARS rights in data provisions.

2.5.9 Instructional Expertise Requirement: Personnel performing data and AI literacy work under this objective shall possess demonstrated expertise in adult learning theory (andragogy), instructional design methodology, and competency-based education principles. Training content shall be designed using evidence-based instructional practices appropriate to adult professional learners in a federal workplace environment. The contractor shall not assign personnel to literacy task orders whose primary expertise is technical data or AI skills without accompanying instructional design qualifications.

Performance Standards and AQLs (Objective 5)

Performance Standard 5.1:

• Standard/Measure: Training modules are role-appropriate, aligned to the adopted literacy framework, and validated through pilot testing.

• AQL: Pilot participant satisfaction rating of ≥80% and Government acceptance of all modules prior to final publication.

Performance Standard 5.2:

• Standard/Measure: The contractor shall conduct a data and AI literacy readiness assessment covering all DoWEA role groups identified by the COR (at minimum: school-level staff, above-school leadership, HQ program owners, data/analytics staff, and regional education researchers). The assessment shall use a standardized instrument accepted by the COR and shall document current competency levels, priority skill gaps, and recommended learning priorities for each role group.

• AQL: Readiness assessment covers 100% of COR-designated role groups. Assessment delivered and accepted by COR within the timeline established in the applicable task order.

Performance Standard 5.3:

• Standard/Measure: The contractor shall develop a multi-year data and AI literacy roadmap aligned with the DoWEA-adopted literacy framework structure. The roadmap shall include, for each role group: learning objectives, competency progressions, delivery models (on-demand, instructor-led, or blended), sequencing, and estimated level of effort.

The roadmap shall be independently executable by Government staff upon delivery.

• AQL: Literacy roadmap covers all COR-designated role groups with all required elements. Roadmap accepted by COR and validated as independently executable through

Government staff review.

Performance Standard 5.4:

• Standard/Measure: Facilitator guides shall be developed for each training module designated for instructor-led delivery. Each guide shall include: session objectives, detailed facilitation instructions, timing guidance, discussion prompts, activity instructions, required materials, and troubleshooting guidance. Facilitator guides shall enable a Government staff member or Educational Technologist with no prior involvement in module development to deliver the training independently.

• AQL: 100% of instructor-led training modules have an accompanying facilitator guide.

Facilitator guides are validated through a dry-run delivery by a Government staff member not involved in module development, with successful delivery confirmed by the COR.

Performance Standard 5.5:

• Standard/Measure: All finalized training materials, facilitator guides, source files, and associated content are published to the Government-designated SharePoint site within 5 business days of COR acceptance. Published materials include editable source files (not

PDF-only) to enable Government modification and updates and are Section 508 compliant. Materials are organized according to the Data and AI Division SharePoint information architecture.

• AQL: 100% of COR-accepted training materials published to Government SharePoint within 5 business days of acceptance. Zero materials published without editable source files.

Performance Standard 5.6:

• Standard/Measure: On-demand data and AI literacy modules shall be developed using multiple delivery modes to maximize accessibility and engagement. At minimum, each

Data and AI Division literacy domain shall include content in at least two of the following formats: interactive e-learning (Articulate Storyline, Rise, or equivalent), written articles or reference guides, voice-over slide presentations, or short-form video.

All on-demand modules shall be designed for self-paced consumption, Section 508 compliant, and published to the Government-designated SharePoint site in formats playable without specialized software or contractor-hosted platforms.

• AQL: 100% of Data and AI Division literacy domains have on-demand content available in at least two distinct delivery modes. Zero modules require contractor-hosted platforms or proprietary software for end-user access.

2.6 Student Information System (SIS) Modernization Support (Objective 6)

The objective is to provide technical support for Student Information System data operations, Ed-Fi integration, data quality management, and continuity of SIS-related data services across the period of performance.

The following tasks help the Contractor understand the requirements to meet the objective but are not exhaustive. The Contractor shall develop a complete solution using a

2.6.1 Support Ed-Fi ODS implementation, including Aspen-to-Ed-Fi export mapping, descriptor customization, and API integration

2.6.2 Provide technical support for optimization of the current Student Information

System (DSIS/Follett Aspen), including configuration improvements, data workflow enhancements, reporting optimization, and resolution of system-level data issues in coordination with the SIS vendor

2.6.3 Develop SIS data quality evaluation program and review processes

2.6.4 Support data migration planning from current SIS to successor system

2.6.5 Support Ed-Fi Data Management Service (DMS) migration planning when the

DMS reaches production readiness

Performance Standards and AQLs (Objective 6)

Performance Standard 6.1:

• Standard/Measure: Ed-Fi integrations use the REST API exclusively (no direct database connections). SIS support deliverables align with Ed-Fi Data Standard specifications.

• AQL: 100% of Ed-Fi integrations use the REST API

Performance Standard 6.2:

• Standard/Measure: Ed-Fi ODS implementation shall achieve validated data synchronization for 100% of education data entities identified in the task order scope.

Each data entity shall complete a validation cycle that includes: source-to-ODS mapping verification, descriptor alignment confirmation, API synchronization testing, and reconciliation against source system counts and values. Validation results shall be documented and accepted by the COR for each data entity before it is considered complete.

• AQL: 100% of in-scope data entities complete the full validation cycle and are accepted by the COR. No data entity is marked complete without documented reconciliation results meeting the ≥99.5% accuracy threshold.

Performance Standard 6.3:

• Standard/Measure: The contractor shall develop an SIS data quality evaluation program aligned to DoWEA data governance standards that defines acceptable quality thresholds, validation rules, and review procedures for each major SIS data domain (enrollment, demographics, attendance, grades, special programs). Program implementation shall include a recurring program review process with defined review cycles, responsible parties, and escalation procedures. Standards and review processes shall be co-developed with Government staff and independently executable by Government staff upon delivery.

• AQL: SIS data quality evaluation program covers 100% of major SIS data domains as defined by the COR. Program review process is validated through at least one

Government-led review cycle prior to task order completion.

2.6.4 SIS Expertise Requirement: Personnel performing SIS modernization support under this objective shall possess demonstrated experience in student information system data management, Ed-Fi data standard integration, and education data interoperability. Experience with the

DoWEA current SIS platform (Follett Aspen) is beneficial, particularly for early task orders focused on the existing environment. As the SIS environment is part of DoWEA's ongoing data modernization, experience with comparable student information systems or Ed-Fi-aligned data platforms is equally relevant to this requirement. The Government does not require, and shall not evaluate proposals on the basis of, experience with any single named SIS product.

2.7 Program Management, Coordination, and Transition Support (Objective 7)

The objective is to provide program management, cross-functional coordination, and transition support to the Data and AI Division in executing the DoWEA Data Modernization Strategy and

AI Strategy.

The following tasks help the Contractor understand the requirements to meet the objective but are not exhaustive. The Contractor shall develop a complete solution using a

2.7.1 Provide a Program Manager as the single point of contact for all active task orders

2.7.2 Develop and maintain integrated master schedules, risk and issue logs, and milestone tracking across concurrent task orders

2.7.3 Provide program planning and execution support for data, analytics, and AI initiatives, including resource planning and schedule development

2.7.4 Support adoption and implementation activities associated with governance, analytics modernization, and AI integration

2.7.5 Support development of communication materials for Data and AI Division initiatives

2.7.6 Coordinate with Government-designated stakeholders across functional areas as directed by the COR

2.7.7 Prepare briefing materials, decision papers, and status reports for senior leadership

2.7.8 Provide transition and knowledge transfer services, including technical operations manuals, training, and parallel operations support

Performance Standards and AQLs (Objective 7)

Performance Standard 7.1:

• Standard/Measure: Status reports are submitted on time with complete and accurate information. Deliverables meet agreed-upon timelines.

• AQL: 95% on-time delivery of status reports and milestone deliverables.

Performance Standard 7.2:

• Standard/Measure: The Program Manager shall maintain an integrated master schedule, risk register, and issue log across all active task orders, updated weekly. Risks shall be identified with probability, impact, and mitigation plan. Issues shall include severity, assigned owner, and target resolution date. The Program Manager shall proactively identify schedule conflicts, resource constraints, and cross-task-order dependencies and present mitigation options to the COR before they impact delivery timelines.

• AQL: Integrated master schedule, risk register, and issue log updated weekly with 100% accuracy against actual status. Zero schedule impacts that were foreseeable and not flagged to the COR at least 10 business days in advance.

Performance Standard 7.3:

• Standard/Measure: Monthly status reports shall include, at minimum: task order status summary by workstream, progress against milestones, updated risk and issue status, financial status (hours burned versus estimated), upcoming 30-day look-ahead, and any decisions or approvals needed from the Government. Briefing materials and decision papers shall be submission-ready for senior leadership without Government rework.

• AQL: 100% of monthly status reports contain all required elements. No more than one (1) status report per quarter returned by the COR for substantive correction or incompleteness.

Performance Standard 7.4:

• Standard/Measure: The contractor shall deliver a Transition Plan no later than 90 days before the end of the IDIQ or final active task order. The Transition Plan shall include:

technical operations manuals for all systems and processes maintained by the contractor, documentation of all active configurations and credentials, knowledge transfer schedule with designated Government recipients, training completion records with competency assessments for each Government recipient, and a parallel operations period of no less than 30 days during which Government staff operate with contractor observation and support. A readiness review shall be conducted at approximately 60 days before contract end to assess Government staff readiness and identify areas requiring additional support.

Knowledge transfer is complete when designated Government staff demonstrate independent operational capability for each transitioned function. The contractor shall provide 30 calendar days of on-call support following formal handoff for escalation of critical issues only.

• AQL: Transition Plan delivered no later than 90 days before contract or final task order end. Readiness review conducted at the 60-day mark. 100% of transitioned functions validated through independent Government staff execution during the parallel operations period. 100% of Government recipients have documented training completion records with competency assessments.

3.0 Constraints

The following are constraints associated with this PWS. Constraints shall be complied entirely during the duration of the contract unless otherwise stated by the Government.

3.1 Non-Personal Services

This is a non-personal services contract. The Government will not exercise direct supervision over contractor personnel. The contractor is responsible for managing its personnel, work schedules, and work execution to meet the requirements of individual task orders.

3.2 Inherently Governmental Functions

The contractor shall not make final policy determinations, approve governance decisions, or exercise inherently governmental authority. All contractor recommendations, analyses, assessments, and deliverables are advisory in nature and subject to Government review and approval. The Government retains all decision-making authority regarding data governance policies, AI adoption decisions, technology selections, procurement recommendations, and organizational strategies. This constraint applies across all functional areas and task orders issued under this IDIQ.

3.3 Government-Designated Platform

The Government has designated Microsoft Fabric and associated Microsoft services (including

Power BI, Microsoft Purview, and Microsoft Entra ID) as the authorized enterprise analytics and data platform. This designation reflects an existing Government infrastructure investment and decision. The contractor shall perform all work within the Government-designated platform environment. This PWS does not require the contractor to propose, evaluate, or implement alternative data platforms. The contractor shall not stand up separate infrastructure outside the

Government-designated environment without written COR approval.

3.4 DevSecOps and Configuration Management

The contractor shall implement infrastructure-as-code and CI/CD deployment pipelines for all

Fabric artifacts, data pipelines, and transformation logic. All transformation logic, pipeline configurations, and infrastructure-as-code templates shall be maintained under version control in a Government-designated repository. The contractor shall implement monitoring, logging, error handling, and alerting for all production data pipelines, dashboards, and data products.

DevSecOps documentation shall be maintained current and included in all transition and knowledge transfer deliverables.

3.5 User Acceptance Testing

No data domain, data product, dashboard, analytics product, or AI capability shall be promoted to production without completing User Acceptance Testing (UAT) with the designated

Government data steward or functional owner. UAT results shall be documented and the designated Government representative shall provide written acceptance prior to production promotion. This requirement applies across all functional areas.

3.6 Security Requirements

All contractor personnel accessing DoWEA systems or data shall meet the following requirements:

• Be United States citizens

• Meet IT Level II position requirements (or as determined by the Government for specific task orders)

• Maintain favorable background investigations (NAC/NACLAC or equivalent)

• Be CAC/PIV eligible and maintain valid credentials throughout the period of performance

• Complete annual DoD Cyber Awareness Challenge training (including elevated privilege requirements where applicable)

• Sign user device acceptance agreements and elevated privileges agreements as required

• Comply with Controlled Unclassified Information (CUI) handling requirements

• Support DoD Zero Trust architecture principles in all work performed

• The contractor shall not store, transfer, or process DoWEA data outside of Government-authorized environments. The contractor must sign all required security agreements prior to accessing any DoWEA system or data. Relevant Microsoft certifications are preferred for technical staff (e.g., Microsoft Certified: Fabric Analytics Engineer, Azure Solutions

Architect, Power BI Data Analyst).

3.6.1 Security Clearance/Privacy Requirements

3.6.1.1 All contract employees providing services under this contract/Performance of Work

Statement (PWS) will Position Designation: Non-Critical Sensitive; Risk Level: Moderate

Risk. For Automatic Data Processing (ADP) / Information Technology (IT) Level II designations, require a Tier 3 background investigation, formerly known as a National

Agency Check with Law and Credit (NACLC) prior to performing on this contract.

3.6.1.2 These positions are considered ADP Level II positions and require access and processing of proprietary/sensitive data on US Government furnished and approved information systems. Compliance with DoW security policies, training requirements instructions, and regulations are mandatory for these positions supporting this contract and performance of work statement.

3.6.1.3 In accordance with DoWM 5200.01-V3, DoW civilians, military members, and onsite support contractors with access to classified/sensitive information shall complete

Information Security refresher training annually.

3.6.1.4 Contractor personnel shall sign a SF 312 Non-Disclosure Agreement and computer/Internet access agreements.

3.6.1.5 All Contractor personnel shall possess the required security certifications and training in accordance with DoW Directive 8570.01.

3.6.1.6 All personal identity verification (PIV) cards, credentials, or badges issued by or through DoWEA shall be returned to DoWEA (COR/SMD), upon termination/completion of this contract or when a contract employee ceases work on this contract, whichever comes first.

3.6.1.7 The Contractor’s facility security officer shall provide a visit access request via

Defense Information Security System (DISS), which replaced the Joint Personnel

Adjudication System (JPAS) as the System of Record to SMO Code: HE12544, for the period of performance, prior to any work on a DoWEA contract. Security Management

Division POC Information: Scott M. Smith Phone: 571-372-0768.

3.6.1.8 School-based personnel shall also meet Child Care Background Check requirements per DoWI 1402.05 and DD 2981.

3.7 Cybersecurity Compliance Requirements—The Contractor shall:

3.7.1 Implement and maintain NIST SP 800 171 controls, conduct continuous monitoring, and provide monthly security status reports.

3.7.2 Encrypt all data in transit and at rest using FIPS 140 2/140 3 validated cryptography;

enforce MFA and Role Based Access Control (RBAC).

3.7.3 Maintain audit logging, vulnerability management (including regular scanning and patching), configuration management, and incident response, including 72-hour breach notification to the Contracting Officer (KO) and DoWEA Cyber.

3.7.4 Submit and maintain Supplier Performance Risk System (SPRS) scores in accordance with DFARS requirements and provide access for Government or authorized third-party assessors.

3.5.4.5 Ensure all subcontractors handling DoWEA data comply with DFARS 252.204

7012, Cybersecurity Maturity Model Certification (CMMC), and all related cybersecurity requirements.

3.5.5.6 Utilize for cloud-based services FedRAMP Moderate/High or DoW-authorized cloud environments for storing/processing CUI and provide all required documentation including data residency/ sovereignty details.

3.5.6.7 Comply with applicable privacy/security standards for student data and execute the

DoWEA Data Privacy Agreement (DPA) when applicable.

• Note: DoWEA/DoW reserves the right to require…